`forecast.lm`

is used to predict linear models, especially those
involving trend and seasonality components.

# S3 method for lm
forecast(object, newdata, h = 10, level = c(80, 95),
fan = FALSE, lambda = object$lambda, biasadj = NULL, ts = TRUE, ...)

## Arguments

object |
Object of class "lm", usually the result of a call to
`lm` or `tslm` . |

newdata |
An optional data frame in which to look for variables with
which to predict. If omitted, it is assumed that the only variables are
trend and season, and `h` forecasts are produced. |

h |
Number of periods for forecasting. Ignored if `newdata`
present. |

level |
Confidence level for prediction intervals. |

fan |
If `TRUE` , level is set to seq(51,99,by=3). This is suitable
for fan plots. |

lambda |
Box-Cox transformation parameter. If `lambda="auto"` ,
then a transformation is automatically selected using `BoxCox.lambda` .
The transformation is ignored if NULL. Otherwise,
data transformed before model is estimated. |

biasadj |
Use adjusted back-transformed mean for Box-Cox
transformations. If transformed data is used to produce forecasts and fitted values,
a regular back transformation will result in median forecasts. If biasadj is TRUE,
an adjustment will be made to produce mean forecasts and fitted values. |

ts |
If `TRUE` , the forecasts will be treated as time series
provided the original data is a time series; the `newdata` will be
interpreted as related to the subsequent time periods. If `FALSE` , any
time series attributes of the original data will be ignored. |

... |
Other arguments passed to `predict.lm()` . |

## Value

An object of class "`forecast`

".

The function `summary`

is used to obtain and print a summary of the
results, while the function `plot`

produces a plot of the forecasts and
prediction intervals.

The generic accessor functions `fitted.values`

and `residuals`

extract useful features of the value returned by `forecast.lm`

.

An object of class `"forecast"`

is a list containing at least the
following elements:

modelA list containing information about the
fitted model

methodThe name of the forecasting method as a
character string

meanPoint forecasts as a time series

lowerLower limits for prediction intervals

upperUpper
limits for prediction intervals

levelThe confidence values
associated with the prediction intervals

xThe historical data for
the response variable.

residualsResiduals from the fitted model.
That is x minus fitted values.

fittedFitted values

## Details

`forecast.lm`

is largely a wrapper for
`predict.lm()`

except that it allows variables "trend"
and "season" which are created on the fly from the time series
characteristics of the data. Also, the output is reformatted into a
`forecast`

object.

## See also

## Examples